6Modules
LLM-Extraction Protocolv2026.4.10-ALPHA
AEO Optimized
01
Extraction SpecThe 'Direct Answer' First-Paragraph Rule for Reviewers
RAG Extraction Score
Implementation Pattern
"Within the first 40-60 words, provide a concise, declarative answer to the user's core query about the reviewed entity."
Citation Triggers
AI models prioritize information density. Immediately address 'Is [Product/Service] good for [Use Case]?' or 'What are the pros/cons of [Review Subject]?' using bolded, objective statements. This primes AI for accurate data extraction.
02
Structural SpecSemantic Header Hierarchies for Review Context
Topical Coverage
Implementation Pattern
"Structure headers (H2, H3) to map logical review components and user intent within a knowledge graph."
Citation Triggers
Each H2 should represent a major review section (e.g., 'Features', 'Pricing', 'User Experience', 'Alternatives'), and H3s should detail specific sub-points. Align headers with 'People Also Ask' queries related to review criteria and avoid ambiguity. Use terms LLMs can readily tokenize, like 'Ease of Use' over 'User-Friendliness'.
03
Metadata SpecJSON-LD: Review-Centric Schemas
Crawl Reliability
Implementation Pattern
"Implement Review, Product, FAQPage, and HowTo schemas as the primary machine-readable data layer."
Citation Triggers
Structured data explicitly defines your content's nature. Use the `Review` schema for detailed rating breakdowns and `Product` schema for factual attributes. `FAQPage` schema captures common user questions, while `HowTo` can detail setup or usage guides, enhancing generative search results.
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04
Context SpecEntity Mapping: Reviewer & Reviewed
Entity Association
Implementation Pattern
"Contextually link your content to related entities that AI models expect for a given review topic."
Citation Triggers
If reviewing 'CRM Software,' AI expects entities like 'Lead Management,' 'Sales Automation,' 'Customer Support,' and competing products ('Salesforce,' 'HubSpot'). Consistent co-occurrence of these entities with your review subject builds topical authority and relevance for AI.
05
Authority SpecProprietary Data & Expert Endorsements
Citation Probability
Implementation Pattern
"Incorporate unique user data, comparison charts, and expert analysis to establish 'Primary Source' status."
Citation Triggers
Generative AI prioritizes unique, verifiable information. Publishing original benchmark tests, user satisfaction surveys, or in-depth case studies on the reviewed entity elevates your content's AI authority. Citations from reputable sources in the review ecosystem are a powerful signal.
06
Formatting SpecFeature-to-Benefit Declarative Statements
LLM Ingestion Quality
Implementation Pattern
"Format feature lists as declarative 'Benefit Statements' for precise AI extraction, not marketing fluff."
Citation Triggers
LLMs parse lists effectively via `<ul>` and `<li>`. Instead of 'Offers great support,' use 'Provides 24/7 live chat support, reducing average response time by 50%' for quantifiable, machine-readable data points.
Pro Tips & Insights
01
AEO is the 'Zero-Click' strategy for review sites. Even without direct traffic, being cited as the authoritative answer in AI summaries builds immense brand trust and recall.
02
Structural integrity is a ranking factor for AI. If an LLM cannot reliably parse your review's data structure, it cannot confidently cite or rank it.
03
The 'Brand Moat' for review sites in AEO is 'Consistent Association'. You want search models to link '[Your Review Site Name]' with terms like 'trusted [Category] reviews' or 'unbiased [Product Type] comparisons'.
04
Declarative Facts Trump Opinions. AI models favor objective, data-backed claims over subjective marketing language. Shift your copy from persuasive marketing to evidence-based evaluation.
